code: def has_padding_token(
      input_tensor: torch.Tensor, tokenizer: PreTrainedTokenizer
  ):
      """Checks if the input tensor contains any padding tokens.
  
      Args:
          input_tensor (torch.Tensor): The input tensor.
          tokenizer (PreTrainedTokenizer): The tokenizer used to encode the input.
  
      Returns:
          bool: True if the input tensor contains any padding tokens, False otherwise.
  
      Example:
          >>> import torch
          >>> from transformers import PreTrainedTokenizer
          >>> tokenizer = PreTrainedTokenizer.from_pretrained('bert-base-uncased')
          >>> input_sentence = "This is an example sentence."
          >>> input_ids = tokenizer.encode(input_sentence, add_special_tokens=True)
          >>> padded_input_ids = torch.nn.functional.pad(input_ids, (0, 10 - len(input_ids)))
          >>> has_padding = has_padding_token(padded_input_ids, tokenizer)
          >>> has_padding
          True
      """
      if input_tensor.dim() == 1:
          return torch.any(
              input_tensor == tokenizer.pad_token_id
          ).item()
      elif input_tensor.dim() == 2:
          return torch.any(
              input_tensor == tokenizer.pad_token_id, dim=-1
          ).item()
      else:
          raise ValueError("Input tensor must be 1D or 2D")
  
imports: import copy
  import logging
  import tempfile
  from typing import Dict, Optional, Tuple, TYPE_CHECKING, Union
  import torch
  import torch.nn.functional
  import transformers
  from bitsandbytes.nn.modules import Embedding
  from packaging import version
  from transformers import AutoConfig, AutoModelForCausalLM, PreTrainedModel, PreTrainedTokenizer, TextStreamer
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID, LOGITS, PREDICTIONS, PROBABILITIES
  from ludwig.schema.trainer import LLMTrainerConfig
  from ludwig.utils.error_handling_utils import default_retry
  from ludwig.utils.logging_utils import log_once
  from ludwig.utils.model_utils import find_embedding_layer_with_path
label_desc: Use image node_id 5 for calling a global function with example usage: has_padding_token(input_tensor, tokenizer) and returns: torch, torch